### What does this PR do?
Type of change: ? New benchmark addition
<!-- Details about the change. -->
This PR adds the following to examples/windows/:
1) FVD (Fréchet Video Distance) evaluation tool — a standalone script
for computing FVD between two sets of videos using a pre-trained I3D
model (Kinetics-400, 1024-dim pooled features).
2) Directory reorganization — moved torch_onnx/ and qad_example/ under a
new diffusers/ folder for clearer grouping of diffusion model examples.
3) Minor fixes: Updated WikiText-2 dataset link in
perplexity_metrics/README.md (old wikitext → Salesforce/wikitext)
### Usage
```python
python compute_fvd.py \
--ref-dir /path/to/reference/videos \
--gen-dir /path/to/generated/videos \
--output results.json \
--device cuda
```
### Testing
Evaluated FVD tool on LTX-2.3 outputs comparing QAD vs PTQ checkpoints
(both NVFp4) against a BF16 baseline across 11 VBench dimensions.
### Before your PR is "*Ready for review*"
Make sure you read and follow [Contributor
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and your commits are signed (`git commit -s -S`).
Make sure you read and follow the [Security Best
Practices](https://github.com/NVIDIA/Model-Optimizer/blob/main/SECURITY.md#security-coding-practices-for-contributors)
(e.g. avoiding hardcoded `trust_remote_code=True`, `torch.load(...,
weights_only=False)`, `pickle`, etc.).
- Is this change backward compatible?: ✅
- If you copied code from any other sources or added a new PIP
dependency, did you follow guidance in `CONTRIBUTING.md`: N/A <!---
Mandatory -->
- Did you write any new necessary tests?: N/A <!--- Mandatory for new
features or examples. -->
- Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?:
N/A <!--- Only for new features, API changes, critical bug fixes or
backward incompatible changes. -->
### Additional Information
<!-- E.g. related issue. -->
<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit
* **New Features**
* Added FVD (Fréchet Video Distance) evaluation: command‑line tool,
I3D‑based feature extraction, PCA option, and quantized comparisons (PTQ
vs QAD) with per‑category and overall results showing QAD's lower
average FVD.
* **Documentation**
* Expanded benchmarks TOC with “Additional Metrics”, added FVD
evaluation guide, usage examples, troubleshooting, and updated
perplexity dataset reference.
* **Chores**
* Added Python requirements for the FVD benchmark.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->
---------
Signed-off-by: ynankani <ynankani@nvidia.com>
Signed-off-by: Yash Nankani <ynankani@nvidia.com>
